Kundu Tanmay, Garg Harish
Department of Mathematics, Chandigarh University, Mohali, Punjab 140413 India.
School of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala, Punjab 147004 India.
Neural Comput Appl. 2022;34(23):20865-20898. doi: 10.1007/s00521-022-07565-y. Epub 2022 Aug 1.
The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching-learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature.
本文的主要目标是提出一种改进的神经网络算法（INNA），用于解决具有非线性资源约束的可靠性冗余分配问题（RRAP）。在这个RRAP中，组件可靠性和冗余分配需要同时考虑。神经网络算法（NNA）是最新且高效的群体优化算法之一，具有强大的全局搜索能力，非常适合解决各种复杂的优化问题。尽管NNA效率高，但它的开发能力较差，导致收敛速度慢，也限制了其在解决优化问题中的实际应用。考虑到这一缺陷，为了在探索和开发之间取得更好的平衡，本文通过实现一种新的对数螺旋搜索算子以及基于教学学习优化（TLBO）的学习阶段搜索策略，对NNA的搜索过程进行了重构，并开发了一种改进的NNA。为了验证INNA的性能，针对七个著名的可靠性优化问题对其进行了评估，并最终与其他现有的元启发式算法进行了比较。此外，还使用Wilcoxon符号秩检验和多重比较检验对INNA的结果进行了统计研究，以表明结果的显著性。实验结果表明，所提出的算法具有很强的竞争力，并且在文献中比先前开发的算法表现更好。
